Supporting quality teaching using educational data mining based on OpenEdX platform

Our lab-based small private online course (SPOC) combined online resources and technology with engagement between faculty and students based on OpenEdX platform. It worked with an auto-grading submission system which could reduce the instructors' burden of evaluation and provide better learners' experience. Different study behaviors were observed from the system tracking logs. Identifying at-risk students becomes timely important in SPOC, and the early prediction can help instructors provide proper supports. In this paper, we focused on extracting features from students' learning activities and study habits for building machine learning models to predict students' performance. We conducted experiments to compare feature importance, and the results showed that study habits related features had played more important role in predicting students' performance. 34 predictive features extracted from Computer Structure Course in Fall 2016, and our model achieved an ROC (Receiver Operating Characteristic Curve)-AUC (area under the curve) in the range of 0.927-0.984 when predicting the performance. Our evaluation showed that data mining is useful in education especially when examining students' learning behavior in online environment, and could support quality teaching. In the next course iteration, we will do A/B testing to determine efficacy for subsequent interventions in a SPOC.